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Open access

Zhijun Zhang, Huali Pan, Gongwen Xu, Yongkang Wang and Pengfei Zhang

Abstract

With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

Open access

Shulin Cheng and Yuejun Liu

Abstract

Document recommendation involves the recommendation of documents similar to those that a user has preferred in the past. The Vector Space Model (VSM) is commonly adopted to denote the document objects and user interests. The user interests are extracted from the documents that a user has browsed. The interest degree of the user is calculated using the TF-IDF method, but the time factor is not considered. The recent documents that a user has browsed embody much more his/her interests. This study proposes a time-aware and grey incidence theory based user interest model to improve document recommendation. First, the time-aware user interest model is proposed based on the analysis of the user interests, document objects and user interest knowledge table. Second, a coefficient vector model of the user interest degree is designed using the grey incidence theory to differentiate the main from the minor user interests. The time-aware and grey incidence theory based user interest model is then exploited to produce document recommendations. Finally, the experiment and evaluation metrics are studied. The results show that the model proposed outperforms other related models and recommends more accurate documents to the users.

Open access

Andrzej Szwabe, Pawel Misiorek, Michal Ciesielczyk and Czeslaw Jedrzejek

Abstract

Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.